This is a supporting role of the R&D team at GEMESYS working on applications of our brain-like chip. You are reporting to our AI engineers.
You will be working with our experts in low-power and edge AI applications who develop new AI solutions for various industries such as healthcare, space, energy, and robotics.
You are responsible for supporting our AI engineers with the design, development, and implementation of advanced machine learning algorithms and models for low-power and edge devices. You work closely with the R&D team and will support the establishment of novel business cases related to the AI applications executable on our hardware.
- Collaborate with software and hardware engineers to implement and test AI models on low-power and edge devices.
- Evaluate and compare the performance of different AI models in low-power and edge environments.
- Work with data scientists and domain experts to develop AI solutions for various industries.
- Maintain an overview of new AI models and algorithms for low-power and edge devices. This includes evaluating their potential application on the in-house AI chip.
- Stay up to date with industry trends, technologies, and best practices related to AI technology.
- Create technical reports and present research findings to the team.
- Prepare and participate in reviews and presentations of reports related to AI applications and solutions with the in-house AI platform.
What you bring:
- Currently pursuing a degree in computer science, electrical engineering, or a related field.
- Experience with machine learning frameworks such as Tensorflow, PyTorch, or Keras.
- Proficiency in the Python programming language.
- Adaptability and willingness to learn new approaches, solutions, and skills.
- Self-motivated, creative, hard-working individual with an entrepreneurial mindset.
- Analytical mindset with the ability to problem solve, work as a team and drive solutions.
- Ability to report and present in English.
- Flexibility and willingness to work in an agile deep-tech start-up.
- Experience in designing and implementing custom AI solutions for embedded systems.
- Broad overview of machine learning algorithms and models.
- Knowledge of software development best practices such as agile methodologies, version control (Git), and code reviews.